Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
Department of Genitourinary Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX.
JCO Precis Oncol. 2024 Oct;8:e2400363. doi: 10.1200/PO.24.00363. Epub 2024 Oct 2.
The primary results of phase III oncology trials may be challenging to interpret, given that results are generally based on value thresholds. The probability of whether a treatment is beneficial, although more intuitive, is not usually provided. Here, we developed and released a user-friendly tool that calculates the probability of treatment benefit using trial summary statistics.
We curated 415 phase III randomized trials enrolling 338,600 patients published between 2004 and 2020. A phase III prior probability distribution for the treatment effect was developed on the basis of a three-component zero-mean mixture distribution of the observed z-scores. Using this prior, we computed the probability of clinically meaningful benefit (hazard ratio [HR] <0.8). The distribution of signal-to-noise ratios and power of phase III oncology trials were compared with that of 23,551 randomized trials from the Cochrane Database.
The signal-to-noise ratios of phase III oncology trials tended to be much larger than randomized trials from the Cochrane Database. Still, the median power of phase III oncology trials was only 49% (IQR, 14%-95%), and the power was <80% in 65% of trials. Using the phase III oncology-specific prior, only 53% of trials claiming superiority (114 of 216) had a ≥90% probability of clinically meaningful benefits. Conversely, the probability that the experimental arm was superior to the control arm (HR <1) exceeded 90% in 17% of trials interpreted as having no benefit (34 of 199).
By enabling computation of contextual probabilities for the treatment effect from summary statistics, our robust, highly practical tool, now posted on a user-friendly webpage, can aid the wider oncology community in the interpretation of phase III trials.
鉴于结果通常基于价值阈值，因此，对于 III 期肿瘤学试验的主要结果进行解释可能具有挑战性。尽管治疗是否有益的概率更直观，但通常不提供。在这里，我们开发并发布了一个用户友好的工具，该工具使用试验汇总统计信息来计算治疗效果的获益概率。
我们整理了 2004 年至 2020 年期间发表的 415 项 III 期随机试验，共纳入 338600 名患者。基于观察到的 z 分数的三组件零均值混合分布，开发了 III 期治疗效果的先验概率分布。使用此先验，我们计算了具有临床意义的获益概率（风险比 [HR] <0.8）。比较了 III 期肿瘤学试验的信号噪声比和功效分布与 Cochrane 数据库中 23551 项随机试验的分布。
III 期肿瘤学试验的信号噪声比往往远大于 Cochrane 数据库中的随机试验。尽管如此，III 期肿瘤学试验的中位功效仅为 49%（IQR，14%-95%），并且在 65%的试验中功效<80%。使用 III 期肿瘤学特定的先验概率，只有 53%（114/216）声称具有优越性的试验具有≥90%的具有临床意义获益的概率。相反，在 17%（34/199）被解释为没有获益的试验中，实验臂优于对照臂（HR <1）的概率超过 90%。
通过从汇总统计数据计算治疗效果的上下文概率，我们的强大且高度实用的工具，现在已发布在用户友好的网页上，可以帮助更广泛的肿瘤学社区解释 III 期试验。
